[ONGOING WORK]

Authors

Roland Bouillot - Université Catholique de Louvain & Maastricht University 

Bertrand Candelon - Université Catholique de Louvain 

Clemens Kool - Maastricht University 

Abstract

Accurate forecasting constitutes a central objective for policymakers. This paper examines the application of advanced machine-learning techniques to predict the 10-year sovereign bond spreads vis-à-vis the German Bund, employing a novel high-dimensional dataset covering 10 European countries over the period 2008-2025. An exhaustive comparison of predictive performance, both in-sample and out-of-sample, demonstrates that XGBoost delivers the highest degree of accuracy. Building on these forecast-based spreads, we construct fragmentation matrices that capture the extent of asymmetry across euro area sovereign bond markets. Prior to the COVID-19 crisis, results confirm the well-documented clustering between core and peripheral countries. However, since 2023 this segmentation appears to have weakened, as French and Belgian spreads exhibit a synchronous trajectory. These findings contribute to the literature on financial integration and fragmentation within the euro area, offering new insights into the evolving dynamics of sovereign bond markets.

Keywords: Machine Learning, Financial Fragmentation, XGBoost, Sovereign spreads 

[ONGOING WORK]

Authors

Roland Bouillot - Université Catholique de Louvain & Maastricht University 

Bertrand Candelon - Université Catholique de Louvain 

Clemens Kool - Maastricht University 

Abstract

This paper studies the financial fragmentation risk in the Euro area sovereign bond market using a combination of machine learning forecasting and network analysis. By applying the XGBoost model to predict sovereign bond yield spreads and constructing correlation-based networks, our analysis identifies structural patterns of financial fragmentation across different forecast horizons. The results indicate that core Euro area economies -such as Austria, Finland and the Netherlands- exhibit strong co-movements in their sovereign spreads, while peripheral countries -including Greece, Ireland and, at longer horizons, France- display weaker correlations. Network analysis highlights key intermediaries in sovereign risk transmission, with Italy and Greece showing strong linkages, while Ireland and Belgium appear to be disconnected. Additionally, we designed a fragmentation indicator, based on betweenness and closeness centrality, that quantifies the degree of financial segmentation over time. Despite policy efforts to enhance financial integration, our findings suggest that fragmentation remains a persistent feature of the Euro area sovereign bond market.

Keywords: Machine Learning, Financial Fragmentation, Network Centrality, Sovereign Spreads, Euro Area

[ONGOING WORK]

Authors

Roland Bouillot - Université Catholique de Louvain & Maastricht University 

Siavash Mohades - Maastricht University & London Business School

Keywords: Monetary Policy, Credit Structure, Financial Fragmentation, Euro Area, Bank Lending